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  Local dendritic balance enables learning of efficient representations in networks of spiking neurons

Mikulasch, F., Rudelt, L., & Priesemann, V. (2021). Local dendritic balance enables learning of efficient representations in networks of spiking neurons. Proceedings of the National Academy of Sciences, 118(50): e2021925118. doi:10.1073/pnas.2021925118.

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Mikulasch, Fabian1, Autor           
Rudelt, Lucas1, Autor           
Priesemann, Viola1, Autor           
Affiliations:
1Max Planck Research Group Neural Systems Theory, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2616694              

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 Zusammenfassung: How can neural networks learn to efficiently represent complex and high-dimensional inputs via local plasticity mechanisms? Classical models of representation learning assume that feedforward weights are learned via pairwise Hebbian-like plasticity. Here, we show that pairwise Hebbian-like plasticity works only under unrealistic requirements on neural dynamics and input statistics. To overcome these limitations, we derive from first principles a learning scheme based on voltage-dependent synaptic plasticity rules. Here, recurrent connections learn to locally balance feedforward input in individual dendritic compartments and thereby can modulate synaptic plasticity to learn efficient representations. We demonstrate in simulations that this learning scheme works robustly even for complex high-dimensional inputs and with inhibitory transmission delays, where Hebbian-like plasticity fails. Our results draw a direct connection between dendritic excitatory–inhibitory balance and voltage-dependent synaptic plasticity as observed in vivo and suggest that both are crucial for representation learning.

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Sprache(n): eng - English
 Datum: 2021-12-072021
 Publikationsstatus: Erschienen
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 Ort, Verlag, Ausgabe: -
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 Identifikatoren: DOI: 10.1073/pnas.2021925118
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Titel: Proceedings of the National Academy of Sciences
Genre der Quelle: Zeitschrift
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Ort, Verlag, Ausgabe: -
Seiten: 10 Band / Heft: 118 (50) Artikelnummer: e2021925118 Start- / Endseite: - Identifikator: ISSN: 0027-8424
ISSN: 1091-6490